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<h1 class="title">Spark Streaming + Kafka Integration Guide</h1>
<p><a href="http://kafka.apache.org/">Apache Kafka</a> is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka.</p>
<ol>
<li>
<p><strong>Linking:</strong> In your SBT/Maven projrect definition, link your streaming application against the following artifact (see <a href="streaming-programming-guide.html#linking">Linking section</a> in the main programming guide for further information).</p>
<pre><code> groupId = org.apache.spark
artifactId = spark-streaming-kafka_2.10
version = 1.1.0
</code></pre>
</li>
<li>
<p><strong>Programming:</strong> In the streaming application code, import <code>KafkaUtils</code> and create input DStream as follows.</p>
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<pre><code> import org.apache.spark.streaming.kafka._
val kafkaStream = KafkaUtils.createStream(
streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume])
</code></pre>
<p>See the <a href="api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$">API docs</a>
and the <a href="https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala">example</a>.</p>
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<pre><code> import org.apache.spark.streaming.kafka.*;
JavaPairReceiverInputDStream<String, String> kafkaStream = KafkaUtils.createStream(
streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume]);
</code></pre>
<p>See the <a href="api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html">API docs</a>
and the <a href="https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java">example</a>.</p>
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<p><em>Points to remember:</em></p>
<ul>
<li>
<p>Topic partitions in Kafka does not correlate to partitions of RDDs generated in Spark Streaming. So increasing the number of topic-specific partitions in the <code>KafkaUtils.createStream()</code> only increases the number of threads using which topics that are consumed within a single receiver. It does not increase the parallelism of Spark in processing the data. Refer to the main document for more information on that.</p>
</li>
<li>
<p>Multiple Kafka input DStreams can be created with different groups and topics for parallel receiving of data using multiple receivers.</p>
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<p><strong>Deploying:</strong> Package <code>spark-streaming-kafka_2.10</code> and its dependencies (except <code>spark-core_2.10</code> and <code>spark-streaming_2.10</code> which are provided by <code>spark-submit</code>) into the application JAR. Then use <code>spark-submit</code> to launch your application (see <a href="streaming-programming-guide.html#deploying-applications">Deploying section</a> in the main programming guide).</p>
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